Control Organoid samples from YFP¶

samples

AD4,AD8,AD12,AD16 are control samples¶

/home/pjb40/jupytervenv/lib/python3.7/site-packages/anndata/_core/anndata.py:21: FutureWarning: pandas.core.index is deprecated and will be removed in a future version.  The public classes are available in the top-level namespace.
  from pandas.core.index import RangeIndex

scanpy==1.5.1 anndata==0.7.1 umap==0.3.10 numpy==1.16.5 scipy==1.4.1 pandas==1.0.1 scikit-learn==0.23.1 statsmodels==0.10.1 python-igraph==0.7.1 louvain==0.6.1

'/n/scratch3/groups/hsph/hbc/pjb40/scratch/TimeSeries_10X/data/velocyto_analysis/Only_controlN_Tumor/control_redo2'

Load filtered matrix¶

with adata and bdata from velocity, also filtered

check the matrix for QC

Normalize and log transform¶

Perform a clustering for normalization in clusters

make the copy of data ****IMP****

normalizing counts per cell
    finished (0:00:00)

extracting highly variable genes
    finished (0:00:02)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)

save the raw data ** IMP **

do the actual filtering

regressing the effect of MT and total count and scaling for this dataset

PCA¶

computing PCA
    on highly variable genes
    with n_comps=50
    finished (0:00:12)

no. of cells in each cluster
DAY-14    5266
DAY-4     1664
DAY-7     1286
DAY-10     983
Name: DAY, dtype: int64

Compute neighborhood graph¶

computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:08)

now compute the umap and then louvain

Embedding neighborhood graph¶

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:26)

computing Diffusion Maps using n_comps=15(=n_dcs)
computing transitions
    finished (0:00:00)
    eigenvalues of transition matrix
    [1.         0.99555653 0.9928839  0.99237084 0.99143106 0.9868962
     0.98214227 0.9810217  0.9789502  0.97598475 0.9730472  0.9705779
     0.96859324 0.96752876 0.9666512 ]
    finished: added
    'X_diffmap', diffmap coordinates (adata.obsm)
    'diffmap_evals', eigenvalues of transition matrix (adata.uns) (0:00:00)
drawing single-cell graph using layout 'fa'
    finished: added
    'X_draw_graph_fa', graph_drawing coordinates (adata.obsm) (0:01:19)

Clustering the neighborhood graph¶

l=['louvain_r0.01','louvain_r0.025','louvain_r0.05','louvain_r0.1','louvain_r0.2','louvain_r0.3','louvain_r0.4' 'louvain_r0.5']

for i in l: res=i.split("_")[1] sc.tl.louvain(adata_pp, resolution=res, key_added=i) print (i,res)

Run the louvain cluters with different resoultions
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 1 clusters and added
    'louvain_r0.01', the cluster labels (adata.obs, categorical) (0:00:01)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 1 clusters and added
    'louvain_r0.025', the cluster labels (adata.obs, categorical) (0:00:01)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 2 clusters and added
    'louvain_r0.05', the cluster labels (adata.obs, categorical) (0:00:01)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 4 clusters and added
    'louvain_r0.1', the cluster labels (adata.obs, categorical) (0:00:01)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 6 clusters and added
    'louvain_r0.2', the cluster labels (adata.obs, categorical) (0:00:01)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 9 clusters and added
    'louvain_r0.3', the cluster labels (adata.obs, categorical) (0:00:00)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 10 clusters and added
    'louvain_r0.4', the cluster labels (adata.obs, categorical) (0:00:01)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished: found 11 clusters and added
    'louvain_r0.5', the cluster labels (adata.obs, categorical) (0:00:00)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:25)

check the qc after clustering¶

plotting the PCA, UMP, diffmap and drawmap

['louvain_r0.01',
 'louvain_r0.025',
 'louvain_r0.05',
 'louvain_r0.1',
 'louvain_r0.2',
 'louvain_r0.3',
 'louvain_r0.4louvain_r0.5']

Plot all the resolutions

how many cells each cluster have

Finding marker genes¶

ranking genes
    finished: added to `.uns['rank_genes_r0.2']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:03)

Plot marker genes

top5 genes in each cluster

the list to check the genes

add the cell annotation and intersect the genes

ploting AT2 genes on UMAP¶

plot the AT2 genes

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

[<matplotlib.axes._subplots.AxesSubplot at 0x2b530f4b6990>,
 <matplotlib.axes._subplots.AxesSubplot at 0x2b530f629750>]

Compare to single cluster : Cluster 1¶

ranking genes
    finished: added to `.uns['rank_genes_groups']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:17)

Other than UMAP, diffmap visulisation also confirms the results

plot how many cells are in each cluster

<matplotlib.legend.Legend at 0x2b530ed55ad0>

start looking at Lyz2 expression is coming from which sample

check for Hmga2

Plots for AT2 markers¶

Club cell markers¶

plots for club cell markers

save the results¶

Dotplots¶

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-2-3a3d5c2d7b7e> in <module>
----> 1 sc.pl.umap(adata_pp, color=['louvain_r0.2','DAY'], frameon=True, use_raw=False )

NameError: name 'sc' is not defined

<bound method AnnData.var_keys of AnnData object with n_obs × n_vars = 9199 × 1529 
    obs: 'DAY', 'batch', 'sample', 'n_counts', 'log_counts', 'n_genes', 'percent_mito', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'Clusters', '_X', '_Y', 'initial_size_unspliced', 'initial_size_spliced', 'initial_size', 'sample_batch', 'louvain_r0.01', 'louvain_r0.025', 'louvain_r0.05', 'louvain_r0.1', 'louvain_r0.2', 'louvain_r0.3', 'louvain_r0.4', 'louvain_r0.5'
    var: 'gene_ids', 'feature_types', 'genome', 'n_cells', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'Accession', 'Chromosome', 'End', 'Start', 'Strand', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
    uns: 'DAY_colors', 'diffmap_evals', 'draw_graph', 'louvain', 'louvain_r0.01_colors', 'louvain_r0.025_colors', 'louvain_r0.05_colors', 'louvain_r0.1_colors', 'louvain_r0.2_colors', 'louvain_r0.3_colors', 'louvain_r0.4_colors', 'louvain_r0.5_colors', 'neighbors', 'pca', 'rank_genes_groups', 'rank_genes_r0.2', 'sample_colors', 'umap'
    obsm: 'X_diffmap', 'X_draw_graph_fa', 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'ambiguous', 'counts', 'matrix', 'spliced', 'unspliced'
    obsp: 'connectivities', 'distances'>

STOP HERE AND DO NOT RUN BELOW. REFERE TO VELOCITY NOTEBOOK.

	louvain_r0.2
0	3399
1	2047
2	1824
3	886
4	816
5	227

	0	1	2	3	4	5
0	Chil1	Clu	Txnrd1	Hopx	Trf	Cp
1	Lamp3	Ffar4	Hsp90aa1	Ager	Hp	Ifitm3
2	Lyz2	Krt7	Ran	Emp2	Epas1	Cbr2
3	Scd1	Ly6a	Gclc	Clic5	Slc34a2	Gstm2
4	Elovl1	S100a6	Tubb5	Sparc	Scd1	Pdcd4

	0_n	1_n	2_n	2_p	3_n	4_n	4_p	5_n	5_p
0	Chil1	Clu	Txnrd1	9.556727e-269	Hopx	Trf	1.188312e-159	Cp	4.855302e-155
1	Lamp3	Ffar4	Hsp90aa1	1.005675e-240	Ager	Hp	4.120648e-129	Ifitm3	3.406921e-134
2	Lyz2	Krt7	Ran	4.452678e-238	Emp2	Epas1	6.338847e-134	Cbr2	3.160241e-95
3	Scd1	Ly6a	Gclc	1.088050e-231	Clic5	Slc34a2	1.362970e-114	Gstm2	6.524714e-89
4	Elovl1	S100a6	Tubb5	2.053580e-219	Sparc	Scd1	3.658975e-112	Pdcd4	5.721815e-69

	0	1	2	3	4	5
Stem	0.0	0.0	0.0	0.0	0.0	0.0
AT2	9.0	7.0	3.0	8.0	9.0	4.0